{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(context=\"notebook\", style=\"whitegrid\", palette=\"dark\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('ex1data1.txt', names=['population', 'profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1101</td>\n",
       "      <td>17.5920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5277</td>\n",
       "      <td>9.1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5186</td>\n",
       "      <td>13.6620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0032</td>\n",
       "      <td>11.8540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8598</td>\n",
       "      <td>6.8233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population   profit\n",
       "0      6.1101  17.5920\n",
       "1      5.5277   9.1302\n",
       "2      8.5186  13.6620\n",
       "3      7.0032  11.8540\n",
       "4      5.8598   6.8233"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97 entries, 0 to 96\n",
      "Data columns (total 2 columns):\n",
      "population    97 non-null float64\n",
      "profit        97 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# take a look at raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x114b9e080>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QSZqAsHxh2fbWZeXbJWVTZkaaBgLT14CzRj20Gng3sBg4feDj56a3OkmavHVrV7JqxaIR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\nOD2njCsUevjWd3fy7R/sor8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Al8UYH0qptno3vG/uAf4QeAPwS0p3B70uxvjTlGqrN+sp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b9ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('population', 'profit', df, size=6, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:tf]",
   "language": "python",
   "name": "conda-env-tf-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
